How AI Data Centers Are Quietly Taking Over the World’s Land, Water, and Energy
The buildings are low-slung and nondescript. From the street, they look like ordinary warehouses. But inside these sprawling facilities, thousands of powerful computers hum around the clock, training the artificial intelligence models that power everything from ChatGPT to Google searches. These are AI data centers, and they’re reshaping communities from Phoenix to rural Oregon in ways most people never see.
While tech companies race to build the next breakthrough in AI, these facilities are consuming natural resources at a pace that’s catching everyone off guard. Large data centers can drink up to 5 million gallons of water per day, equivalent to the usage of a town with 10,000 to 50,000 people. The electricity they require could soon rival that of entire countries. And the land they occupy is transforming local economies and environments faster than regulators can respond.
This isn’t a distant future scenario. Right now, in water-stressed regions across the American West, roughly two-thirds of new data centers built or in development since 2022 are located in areas already grappling with high levels of water stress. Communities are waking up to discover that their drinking water and power supply are being stretched to accommodate the computational needs of AI systems most residents will never directly use. The quiet expansion of data center infrastructure represents one of the most significant resource challenges of our time, yet it’s happening largely out of public view.
The Hidden Scale of AI Data Center Growth
The numbers tell a startling story. Scientists estimate that power requirements of data centers in North America increased from 2,688 megawatts at the end of 2022 to 5,341 megawatts at the end of 2023, driven primarily by generative AI demands. This isn’t gradual growth. It’s exponential.
Microsoft’s new Fairwater AI datacenter in Wisconsin covers 315 acres and houses 1.2 million square feet under roof. The facility required 46.6 miles of deep foundation piles and 26.5 million pounds of structural steel. This single location represents the kind of massive infrastructure investment happening across the country, often in communities unprepared for the environmental impact.
Why AI Needs So Much More Than Traditional Computing
AI workloads differ fundamentally from regular computing tasks. A generative AI training cluster might consume seven or eight times more energy than a typical computing workload. Training large language models requires thousands of GPUs running continuously for months, processing billions of calculations simultaneously.
Think of it this way: hosting your email or a website uses relatively modest computing power. But training an AI model to understand and generate human language? That requires massive parallel processing that generates extreme heat and demands enormous amounts of electricity. The more sophisticated the AI, the greater the resource consumption.
Water Consumption: The Crisis No One Saw Coming
Here’s something most people don’t realize: Each 100-word AI prompt is estimated to use roughly one bottle of water. Multiply that by billions of queries entered into systems like ChatGPT every day, and the scale becomes staggering.
The Three Types of Water Footprint
A data center’s water footprint includes on-site water usage, water used by power plants supplying electricity to data centers, and water consumed during chip manufacturing. Most people only think about the first category, but the indirect water consumption from electricity generation is actually much larger.
Data center water consumption breaks down like this:
- Direct use for cooling: A medium-sized data center can consume up to roughly 110 million gallons of water per year for cooling purposes, equivalent to the annual water usage of approximately 1,000 households.
- Indirect use from power generation: Between 45 percent and 60 percent of data center water consumption comes from electricity generation at thermoelectric and hydroelectric plants.
- Manufacturing: Producing a single microchip requires 8-10 liters of water
Evaporative Cooling: Cheap But Thirsty
Most data centers rely on evaporative cooling systems because they’re cost-effective and efficient. Data centers typically evaporate about 80% of the water they draw, discharging only 20% back to wastewater treatment facilities. This means the vast majority of water consumed never returns to the local watershed.
The problem intensifies during heat waves, exactly when water is most scarce and communities need it most. In Phoenix, where temperatures regularly exceed 115 degrees, the Salt River Project utility reported 7,000 megawatts of data center requests in their pipeline, with each facility requiring massive cooling infrastructure.
Energy Demand: Powering the AI Revolution
The electricity requirements for AI data centers are staggering. By 2026, electricity consumption of data centers is expected to approach 1,050 terawatt-hours, which would place them fifth globally between Japan and Russia. To put that in perspective, that’s enough power to run tens of millions of homes.
The Growing Power Crisis
By 2030 to 2035, data centers could account for 20% of global electricity use. This explosive growth is straining power grids that were never designed to handle such concentrated demand. There’s currently a seven-year wait on some requests for connection to the grid, creating bottlenecks that slow development but don’t stop it.
In Arizona alone, data centers use 7.4 percent of the state’s power, while in Oregon they consume 11.4 percent. These percentages continue climbing as more facilities come online.
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The Fossil Fuel Connection
Despite ambitious renewable energy pledges from tech companies, the pace of data center expansion means most new facilities rely on fossil fuel-based electricity. Researchers project that by 2028, AI data centers could consume around 300 terawatts of energy annually, enough to power all California households twice over for a year.
Companies release new AI models every few weeks, so the energy used to train prior versions goes to waste. This short model lifespan amplifies the environmental impact, as each generation typically requires more training power than the last.
Land Use: Reshaping Communities
The physical footprint of AI infrastructure extends far beyond individual buildings. These facilities require:
- Hundreds of acres for campus-style developments
- Proximity to reliable power transmission lines
- Access to water sources for cooling
- Robust internet connectivity infrastructure
- Support facilities for maintenance and operations
In Santa Clara’s Silicon Valley, one planning commission chair noted they’ve reached 60 data centers and questioned whether they want the city “paved over with data centers, edge to edge”. The concern reflects a broader tension: these facilities take up enormous amounts of land but provide relatively few local jobs compared to other industries.
Federal Land Grabs
The competition for suitable locations has intensified. The U.S. Department of Energy announced plans to develop AI data centers on federal lands at four sites: Idaho National Laboratory, Oak Ridge Reservation, Paducah Gaseous Diffusion Plant, and Savannah River Site. This represents a new phase where previously protected federal lands are being opened for private data center development.
The Environmental Cost Beyond Resources
The resource consumption creates cascading environmental effects:
Carbon emissions: Researchers from Harvard and the University of Pisa reported that U.S. data centers produced 105 million tons of CO2 equivalent gases with a carbon intensity 48 percent higher than the national average. Data center emissions now represent about 2.18 percent of national carbon emissions.
Electronic waste: The short lifespan of GPUs and specialized computing hardware creates mounting e-waste problems. Advanced chips become obsolete within a few years, leading to disposal challenges for components that required significant resources to manufacture.
Local ecosystem disruption: Withdrawing large volumes of freshwater from streams and aquifers can lead to aquifer depletion, particularly in already water-stressed regions.
Who Pays? The Cost Shifting to Consumers
All this new demand, plus the infrastructure needed to support it, is raising electricity rates for residential customers across the country. Families are essentially subsidizing AI’s growth through their utility bills, while tech companies reap the profits from AI services.
The economic equation doesn’t balance locally either. Phoenix’s deputy city manager noted that “a data center takes a lot of land but they don’t provide enough jobs for the infrastructure investment”. Communities invest in upgraded water systems, expanded electrical grids, and new transmission lines, but see limited employment benefits in return.
The Transparency Problem
One of the biggest challenges is that no one knows the full scope of the problem. There are no uniform regulatory requirements for data center operators to track and report their water use. A 2016 report found that fewer than one-third of data center operators track water consumption.
Without comprehensive data, communities can’t make informed decisions about development. Tech companies have been reluctant to disclose detailed water consumption and energy consumption figures, often citing competitive concerns. This opacity makes it nearly impossible to assess true environmental impacts or plan for sustainable growth.
Emerging Regulations
Some jurisdictions are fighting back. In 2024, the EU imposed water and energy use reporting requirements on data centers operating in EU regions through the Energy Efficiency Directive. California is considering legislation that would require data center companies to estimate expected water use when applying for permits.
These represent important first steps, but regulation remains fragmented and insufficient to address the scale of expansion happening globally.
Potential Solutions and Innovations
The situation isn’t hopeless. Several technologies and approaches could significantly reduce the environmental impact:
Advanced cooling systems: Liquid immersion cooling, where servers are submerged in specialized fluids, can reduce both water and energy consumption. Water-cooled data centers consume about 10 percent less energy than air-cooled data centers.
Closed-loop systems: Some advanced designs recycle cooling water completely, eliminating the need to tap drinking water supplies. These “zero water” designs cost more upfront but eliminate ongoing water consumption.
Waste heat reuse: Progressive facilities are capturing waste heat and using it for district heating systems. Finland and other Nordic countries have pioneered this approach, with some facilities using waste heat to warm nearby homes.
Strategic location: Building data centers in cooler climates reduces cooling needs substantially. However, this conflicts with desires to locate facilities near users and existing infrastructure.
More efficient AI: Research into more efficient algorithms and specialized chips could reduce computational requirements. Recent developments suggest training efficiency could improve significantly with better model architectures.
The Policy Challenge
Policymakers face difficult tradeoffs. AI development promises significant economic benefits and national security advantages. AI is expected to have large effects across the economy, including healthcare, transportation, and education. Restricting data center growth could mean ceding AI leadership to other nations.
Yet unchecked expansion threatens water security, strains electrical grids, accelerates climate change, and burdens communities with infrastructure costs while providing limited local benefits. Finding the right balance requires transparent data, thoughtful regulation, and genuine commitment to sustainability from tech companies.
Senator Edward Markey has introduced bipartisan legislation to establish federal standards and voluntary reporting guidelines for measuring AI’s environmental footprint. Whether such measures gain traction remains to be seen, particularly as political pressure mounts to accelerate rather than regulate AI development.
Conclusion
AI data centers are fundamentally reshaping how we use land, water, and energy resources worldwide. These facilities are consuming resources at a pace that threatens water security in drought-prone regions, strains electrical grids, and contributes significantly to carbon emissions.
With projections showing data center energy consumption could reach 20% of global electricity use by 2035 and water consumption potentially doubling by 2030, the expansion represents one of the most significant environmental challenges of our time.
The quiet nature of this transformation makes it particularly concerning—most communities don’t realize the scale of resource consumption happening in their backyards until infrastructure problems emerge. Without transparent reporting requirements, sustainable cooling technologies, and thoughtful regulation that balances innovation with environmental protection, the AI revolution risks creating a resource crisis that undermines the very progress it promises to deliver.
